Guessing sequences of eigenvectors for LMPs defining spectrahedral relaxations of Eulerian rigidly convex sets
Alejandro Gonz\'alez Nevado

TL;DR
This paper introduces a novel approach to approximating Eulerian rigidly convex sets using spectrahedral relaxations, leveraging eigenvector guessing sequences to improve bounds on the accuracy of these approximations.
Contribution
It develops a new sequence of vectors that significantly improves the bounds on the roots of Eulerian polynomials, enhancing the accuracy of spectrahedral relaxations.
Findings
New bounds outperform previous literature bounds.
Constructed vector sequences exhibit exponential growth in accuracy improvement.
Enhanced spectral approximation of Eulerian convex sets.
Abstract
Stable multivariate Eulerian polynomials were introduced by Br\"and\'en. Particularizing some variables, it is possible to extract real zero multivariate Eulerian polynomials from them. These real zero multivariate Eulerian polynomials can be fed into constructions of spectrahedral relaxations providing therefore approximations to the (Eulerian) rigidly convex sets defined by these polynomials. The accuracy of these approximations is measured through the behaviour in the diagonal, where the usual univariate Eulerian polynomials sit. In particular, in this sense, the accuracy of the global spectrahedral approximation produced by the spectrahedral relaxation can be measured in terms of bounds for the extreme roots of univariate Eulerian polynomials. The bounds thus obtained beat the previous bounds found in the literature. However, the bound explicitly studied and obtained before beat the…
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Taxonomy
TopicsSynthesis and properties of polymers
